The process of blood passing through the tissues is called perfusion and is one of the most fundamental physiological quantifiables. Disorder of perfusion is a process leading to mammal disease and mortality.
Normal brain function requires a continuous supply of oxygen to meet the metabolic demands of activity. The regional availability of oxygen in brain tissue is traditionally inferred from the magnitude of cerebral blood flow (CBF) and the concentration of oxygen in arterial blood. CBF is sensitive to regional levels of neuronal activity—known as neurovascular coupling—and methods to detect changes in CBF therefore provide powerful means of mapping brain function.
In disease, measurements of CBF are widely used in the evaluation of patients suspected of acute ischemic stroke and in patients with flow-limiting steno-occlusive diseases of their carotid arteries, due to well-defined thresholds for sustaining neuronal firing and development of permanent infarction.
The standard method of estimating CBF is currently singular value decomposition (SVD), available in several variants. In short, the SVD technique for estimating CBF is essentially based on deconvolving the arterial input function (AIF) with the concentration time curve using singular value decomposition (SVD) to estimate the impulse response (defined as the residue function multiplied by CBF). Its maximum value is then the CBF.
However, SVD is known to underestimate high flow components and to be sensitive to delays in the arterial input function (AIF), which may compromise physiological interpretation of the perfusion indices obtained by SVD. Needless to say, this could be very critical for the diagnosis of patients having a severe condition, e.g. acute stroke where CBF is used is to delineate the ischemic regions.
Recently, Kim Mouridsen et al., NeuroImage, 33 (2006), 570-579, proposed an alternative model approach using a physiological model of the transport in the capillaries. Using Bayesian modeling, an estimate of hemodynamic parameters can be obtained. More specifically, a parametric model of the residue function with a gamma-type function is used. The obtained results compare well to the SVD methods for low flows, but do not underestimate high flows and is comparatively not as affected by noise as SVD. However, the results are still somewhat sensitive to noise and rely on general numerical approximation schemes which may compromise precision of estimates and result in clinically infeasible computing times, thus limiting practical implementation.
Hence, an improved method for estimating perfusion indices would be advantageous, and in particular a more efficient and/or reliable method for estimating perfusion indices would be advantageous.